• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

一种基于内部网的医院感染监测自动化系统:与医生自我报告相比的前瞻性验证

An intranet-based automated system for the surveillance of nosocomial infections: prospective validation compared with physicians' self-reports.

作者信息

Bouam Samir, Girou Emmanuelle, Brun-Buisson Christian, Karadimas Harry, Lepage Eric

机构信息

Département de Biostatistiques et d'Information Hospitalier, Hôpital Henri Mondor, Assistance Publique-Hôpitaux de Paris, Créteil, France.

出版信息

Infect Control Hosp Epidemiol. 2003 Jan;24(1):51-5. doi: 10.1086/502115.

DOI:10.1086/502115
PMID:12558236
Abstract

OBJECTIVE

To examine the reliability of the data produced by an automated system for the surveillance of nosocomial infections.

SETTING

A 906-bed, tertiary-care teaching hospital.

DESIGN

Three surveillance techniques were concurrently performed in seven high-risk units during an 11-week period: automated surveillance (AS) based on the prospective processing of computerized medical records; laboratory-based ward surveillance (LBWS) based on the retrospective verification by ward clinicians of weekly reports of positive bacteriologic results; and a reference standard (RS) consisting of the infection control team reviewing case records of patients with positive bacteriology results. Bacteremia, urinary tract infections, and catheter-related infections were recorded for all inpatients. The performances (sensitivity, specificity, and time consumption) of AS and LBWS were compared with those of RS.

RESULTS

Of 548 positive bacteriology samples included during the study period, 229 (42%) were classified as nosocomial infections. The overall sensitivity was 91% and 59% for AS and LBWS, respectively. The two methods had the same overall specificity value (91%). Kappa measures of agreement were 0.81 and 0.54 for AS and LBWS, respectively. AS required less time to collect data (54 seconds per week per unit) compared with LBWS (7 minutes and 43 seconds per week per unit) and RS (37 minutes and 15 seconds per week per unit).

CONCLUSION

Our results confirm that the retrospective review of charts and laboratory data by physicians lacks sensitivity for the surveillance of nosocomial infections. The intranet-based automated method developed for this purpose was more accurate and less time-consuming than the weekly, retrospective LBWS method.

摘要

目的

检验用于监测医院感染的自动化系统所产生数据的可靠性。

地点

一家拥有906张床位的三级护理教学医院。

设计

在11周的时间里,同时在七个高危科室采用三种监测技术:基于计算机化病历前瞻性处理的自动化监测(AS);基于病房临床医生对每周阳性细菌学结果报告进行回顾性核实的基于实验室的病房监测(LBWS);以及由感染控制团队审查细菌学结果呈阳性患者的病例记录组成的参考标准(RS)。记录所有住院患者的菌血症、尿路感染和导管相关感染情况。将AS和LBWS的性能(敏感性、特异性和时间消耗)与RS的性能进行比较。

结果

在研究期间纳入的548份阳性细菌学样本中,229份(42%)被归类为医院感染。AS和LBWS的总体敏感性分别为91%和59%。两种方法的总体特异性值相同(91%)。AS和LBWS的Kappa一致性度量分别为0.81和0.54。与LBWS(每周每科室7分43秒)和RS(每周每科室37分15秒)相比,AS收集数据所需时间更少(每周每科室54秒)。

结论

我们的结果证实,医生对图表和实验室数据的回顾性审查在监测医院感染方面缺乏敏感性。为此开发的基于内部网的自动化方法比每周进行的回顾性LBWS方法更准确且耗时更少。

相似文献

1
An intranet-based automated system for the surveillance of nosocomial infections: prospective validation compared with physicians' self-reports.一种基于内部网的医院感染监测自动化系统:与医生自我报告相比的前瞻性验证
Infect Control Hosp Epidemiol. 2003 Jan;24(1):51-5. doi: 10.1086/502115.
2
Evaluation of two retrospective active surveillance methods for the detection of nosocomial infection in surgical patients.
Infect Control Hosp Epidemiol. 2000 Jan;21(1):24-7. doi: 10.1086/501692.
3
Preliminary assessment of an automated surveillance system for infection control.感染控制自动化监测系统的初步评估
Infect Control Hosp Epidemiol. 2004 Apr;25(4):325-32. doi: 10.1086/502400.
4
Implementation of a novel on-ward computer-assisted surveillance system for device-associated infections in an intensive care unit.在重症监护病房实施一种新型的病房内设备相关感染计算机辅助监测系统。
Int J Hyg Environ Health. 2008 Mar;211(1-2):192-9. doi: 10.1016/j.ijheh.2007.02.001. Epub 2007 Jun 19.
5
Representativeness of the surveillance data in the intensive care unit component of the German nosocomial infections surveillance system.重症监护病房部分德国医院感染监测系统监测数据的代表性。
Infect Control Hosp Epidemiol. 2010 Sep;31(9):934-8. doi: 10.1086/655462.
6
[Nosocomial infections in a university hospital. Results of a prospective study of infections in a medical and surgical ward and a surgical intensive care unit].[大学医院的医院感染。内科与外科病房及外科重症监护病房感染的前瞻性研究结果]
Schweiz Med Wochenschr. 1983 Dec 3;113(48):1782-90.
7
How many nosocomial infections are missed if identification is restricted to patients with either microbiology reports or antibiotic administration?如果仅将感染鉴定局限于有微生物学报告或接受抗生素治疗的患者,会漏诊多少医院感染病例?
Infect Control Hosp Epidemiol. 1999 Feb;20(2):124-7. doi: 10.1086/501600.
8
Comparison of automated strategies for surveillance of nosocomial bacteremia.医院获得性菌血症监测自动化策略的比较
Infect Control Hosp Epidemiol. 2007 Sep;28(9):1030-5. doi: 10.1086/519861. Epub 2007 Jun 28.
9
[Diagnostic training for the surveillance of nosocomial infections: what is possible and significant?].[医院感染监测的诊断培训:哪些是可行且重要的?]
Zentralbl Hyg Umweltmed. 1998 Jun;201(2):153-66.
10
The effect of frequency of chart review on the sensitivity of nosocomial infection surveillance in general surgery.
Infect Control Hosp Epidemiol. 1999 Mar;20(3):208-12. doi: 10.1086/501615.

引用本文的文献

1
Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review.基于电子健康记录数据实现术后感染的自动识别以进行预测和监测:范围综述
JMIR Med Inform. 2024 Sep 10;12:e57195. doi: 10.2196/57195.
2
Systematic scoping review of automated systems for the surveillance of healthcare-associated bloodstream infections related to intravascular catheters.系统评价自动化系统用于监测与血管内导管相关的医源性血流感染。
Antimicrob Resist Infect Control. 2024 Feb 28;13(1):25. doi: 10.1186/s13756-024-01380-x.
3
Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.
使用临床文本进行机器学习的脓毒症预测、早期检测和识别:系统评价。
J Am Med Inform Assoc. 2022 Jan 29;29(3):559-575. doi: 10.1093/jamia/ocab236.
4
Electronically assisted surveillance systems of healthcare-associated infections: a systematic review.电子辅助医疗相关感染监测系统:系统评价。
Euro Surveill. 2020 Jan;25(2). doi: 10.2807/1560-7917.ES.2020.25.2.1900321.
5
Electronic Surveillance For Catheter-Associated Urinary Tract Infection Using Natural Language Processing.使用自然语言处理技术对导管相关尿路感染进行电子监测
AMIA Annu Symp Proc. 2018 Apr 16;2017:1507-1516. eCollection 2017.
6
A Web-Based, Hospital-Wide Health Care-Associated Bloodstream Infection Surveillance and Classification System: Development and Evaluation.基于 Web 的全院级医疗相关性血流感染监测与分类系统:开发与评估。
JMIR Med Inform. 2015 Sep 21;3(3):e31. doi: 10.2196/medinform.4171.
7
Clinical microbiology informatics.临床微生物信息学
Clin Microbiol Rev. 2014 Oct;27(4):1025-47. doi: 10.1128/CMR.00049-14.
8
Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review.二十一世纪电子监控医疗相关感染中数据的使用与效果:系统综述。
J Am Med Inform Assoc. 2014 Sep-Oct;21(5):942-51. doi: 10.1136/amiajnl-2013-002089. Epub 2014 Jan 13.
9
Utilization of electronic medical records to build a detection model for surveillance of healthcare-associated urinary tract infections.利用电子病历构建医疗相关尿路感染监测的检测模型。
J Med Syst. 2013 Apr;37(2):9923. doi: 10.1007/s10916-012-9923-2. Epub 2013 Jan 17.
10
Effectiveness of an automated surveillance system for intensive care unit-acquired infections.重症监护病房获得性感染的自动化监测系统的效果。
J Am Med Inform Assoc. 2013 Mar-Apr;20(2):369-72. doi: 10.1136/amiajnl-2012-000898. Epub 2012 Aug 7.